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Towards Unified Multimodal Models:Trends and Insights
Recent advancements in unified models for multimodal understanding and generation, including works like Transfusion, Next-GPT, and EMU3, highlight the trend towards frameworks capable of diverse tasks. These models typically fall into two categories:single models, which use a unified architecture to process multiple modalities, and multi-expert models, where specialized sub-models are used for each modality. Multi-expert models include different alignment strategies such as Image-Centric Alignment (e.g., ImageBind), Text-Centric Alignment (e.g., Next-GPT), and others, each focusing on aligning specific modalities for more flexible integration. The single models explore techniques like pure autoregressive methods, diffusion-based approaches, or a combination of both. This blog provides a comprehensive overview of unified multimodal models, reviewing current developments and discussing key design principles, including the use of autoregressive and diffusion mechanisms.